{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "07a8e0d8",
   "metadata": {},
   "source": [
    "## Modified introduction using forex data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db9723dc",
   "metadata": {},
   "source": [
    "This is the trading rule example shown in [the introduction](https://github.com/robcarver17/pysystemtrade/blob/master/docs/introduction.md) but modified to use Interactive Brokers instead of CSV files as data source.  \n",
    "\n",
    "IB requires a minimum equity and a monthly subscription to provide historical data on future contracts.  This example was modified to use FX prices instead futures to make it runnable with free unfunded paper trading accounts.  Note that Rob [does not recommend trading FX spot data with IB due to their high fees](https://github.com/robcarver17/pysystemtrade/issues/517#issuecomment-1010770678)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d855c448",
   "metadata": {},
   "source": [
    "First, import the required packages and initialize ib_insync.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3b6f4071",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sysbrokers.IB.ib_connection import connectionIB\n",
    "from sysbrokers.IB.ib_Fx_prices_data import ibFxPricesData\n",
    "\n",
    "from ib_insync import util\n",
    "util.startLoop() #only required when running inside a notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70963367",
   "metadata": {},
   "source": [
    "Connecting to Interactive Brokers gateway..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4ce91bcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IB broker connection{'ipaddress': 'localhost', 'port': 7497, 'client': 111}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conn = connectionIB(111)\n",
    "conn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77a3526b",
   "metadata": {},
   "source": [
    "See what fx instruments we have configured.  These are configured in `sysbrokers/IB/ib_config_spot_FX.csv`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "86db67ca",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AUDUSD',\n",
       " 'CADUSD',\n",
       " 'CHFUSD',\n",
       " 'EURUSD',\n",
       " 'GBPUSD',\n",
       " 'HKDUSD',\n",
       " 'JPYUSD',\n",
       " 'KRWUSD',\n",
       " 'CNHUSD',\n",
       " 'SGDUSD']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ibfxpricedata = ibFxPricesData(conn)\n",
    "ibfxpricedata.get_list_of_fxcodes()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2dbdd12",
   "metadata": {},
   "source": [
    "Now we select one instrument (`EURUSD`) and try to fetch historical data for it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6748a9c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index\n",
       "2021-01-13 23:00:00    1.215745\n",
       "2021-01-14 23:00:00    1.215415\n",
       "2021-01-15 23:00:00    1.207550\n",
       "2021-01-18 23:00:00    1.207725\n",
       "2021-01-19 23:00:00    1.212885\n",
       "                         ...   \n",
       "2022-01-06 23:00:00    1.129785\n",
       "2022-01-07 23:00:00    1.136100\n",
       "2022-01-10 23:00:00    1.132565\n",
       "2022-01-11 23:00:00    1.136725\n",
       "2022-01-12 23:00:00    1.144345\n",
       "Name: FINAL, Length: 261, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ibfxpricedata.get_fx_prices('EURUSD')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b480ed7e",
   "metadata": {},
   "source": [
    "Data can also be indexed as a python dict:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b05e7852",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index\n",
       "2021-01-13 23:00:00    0.009627\n",
       "2021-01-14 23:00:00    0.009633\n",
       "2021-01-15 23:00:00    0.009627\n",
       "2021-01-18 23:00:00    0.009644\n",
       "2021-01-19 23:00:00    0.009624\n",
       "                         ...   \n",
       "2022-01-06 23:00:00    0.008633\n",
       "2022-01-07 23:00:00    0.008653\n",
       "2022-01-10 23:00:00    0.008680\n",
       "2022-01-11 23:00:00    0.008673\n",
       "2022-01-12 23:00:00    0.008724\n",
       "Name: FINAL, Length: 261, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ibfxpricedata['JPYUSD']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86a059a1",
   "metadata": {},
   "source": [
    "Create the trading rule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ab5b324a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sysquant.estimators.vol import robust_vol_calc\n",
    "\n",
    "\n",
    "def calc_ewmac_forecast(price, Lfast, Lslow=None):\n",
    "    \"\"\"\n",
    "    Calculate the ewmac trading rule forecast, given a price and EWMA speeds Lfast, Lslow and vol_lookback\n",
    "\n",
    "    \"\"\"\n",
    "    if Lslow is None:\n",
    "        Lslow = 4 * Lfast\n",
    "\n",
    "    ## We don't need to calculate the decay parameter, just use the span directly\n",
    "    fast_ewma = price.ewm(span=Lfast).mean()\n",
    "    slow_ewma = price.ewm(span=Lslow).mean()\n",
    "    raw_ewmac = fast_ewma - slow_ewma\n",
    "\n",
    "    vol = robust_vol_calc(price.diff())\n",
    "\n",
    "    return raw_ewmac / vol"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44f122dc",
   "metadata": {},
   "source": [
    "Run a forecast with the previous rule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e09da640",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index\n",
       "2022-01-06 23:00:00   -5.065233\n",
       "2022-01-07 23:00:00   -4.766807\n",
       "2022-01-10 23:00:00   -4.739162\n",
       "2022-01-11 23:00:00   -4.633853\n",
       "2022-01-12 23:00:00   -4.220275\n",
       "Name: FINAL, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price=ibfxpricedata['EURUSD']\n",
    "ewmac=calc_ewmac_forecast(price, 32, 128)\n",
    "ewmac.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fd8e9ae2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(12,5))\n",
    "\n",
    "ax1 = price.plot(color='blue', grid=True, label='Price')\n",
    "ax2 = ewmac.plot(color='red', grid=True, secondary_y=True, label='Forecast')\n",
    "\n",
    "h1, l1 = ax1.get_legend_handles_labels()\n",
    "h2, l2 = ax2.get_legend_handles_labels()\n",
    "\n",
    "\n",
    "plt.legend(h1+h2, l1+l2, loc=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04d63dc1",
   "metadata": {},
   "source": [
    "The original introduction jumps directly to \"Did we make any money?\".  \n",
    "I would like to see here the orders that were triggered by this forecast, but instead we jump directly into P&L.  Still, these are the P&L numbers for this forecast and data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2939bcdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[('min', '-1.3'),\n",
       "  ('max', '2.03'),\n",
       "  ('median', '0.001141'),\n",
       "  ('mean', '0.00234'),\n",
       "  ('std', '0.3203'),\n",
       "  ('skew', '0.8017'),\n",
       "  ('ann_mean', '0.5691'),\n",
       "  ('ann_std', '5.125'),\n",
       "  ('sharpe', '0.111'),\n",
       "  ('sortino', '0.1469'),\n",
       "  ('avg_drawdown', '-1.24'),\n",
       "  ('time_in_drawdown', '0.9315'),\n",
       "  ('calmar', '0.1697'),\n",
       "  ('avg_return_to_drawdown', '0.4591'),\n",
       "  ('avg_loss', '-0.1954'),\n",
       "  ('avg_gain', '0.1832'),\n",
       "  ('gaintolossratio', '0.9377'),\n",
       "  ('profitfactor', '1.025'),\n",
       "  ('hitrate', '0.5223'),\n",
       "  ('t_stat', '0.115'),\n",
       "  ('p_value', '0.9085')],\n",
       " ('You can also plot / print:',\n",
       "  ['rolling_ann_std', 'drawdown', 'curve', 'percent'])]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from systems.accounts.account_forecast import pandl_for_instrument_forecast\n",
    "account = pandl_for_instrument_forecast(forecast = ewmac, price = price)\n",
    "account.percent.stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "708c4bfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E7tY4Uo09IXF9wJ9GjV0Du1IqbyRr7IXDuxTORAOBffTumIlRr4E01kDVwK6Uyhtd/RF8bmdy8q+ZLjHCdKyMPdGH3h/SjF0pNQd1BSLJHjGzQYm9cMeYGbtHM3al1BzWFQiPOux/JiqeSCmmIBHYNWNXSs1BXf2RQSM6Z7qJ1NhdTgcelwO/ZuxKqbmoKxCeXYE9UWMf55pAkdtJQGvsSqm5qLs/Qtks6REDE6uxg3UBVTN2pdScY4yxL57Onox9oFfMOCs8eZz0T3WNXUR+ICItIrI3F8dTSql0+cMxonFDxSwK7InphYcuvjGUlbFPfSnmTuCaHB1LKaXS1pWYJ2YWlWIuX17FHz90JSvmFY+5n8/jnPopBYwxTwAduTiWUkplIjHqdLZMJwDWWqtrFow/l01hwfRk7EopNa0GphOYPYF9onwe58wcoCQit4rIdhHZ3traOlXNKqXmiLNdAYBZNfJ0oorcrpk5QMkYc4cxZrMxZnNNTfarkCilVMJfXmzlM/fuY0lVEUuqiqb7dHLO556GGrtSSk2n7z15jJoSD/e8//Jxuw7ORkVu59TX2EXkF8DTwGoROSMi783FcZVSaiKOtvRxcUNlco3TfFOU5pqtOVlByRjzllwcRyml0uUPRWnsDrK8xjfdpzJpfCMsnTcWLcUopWa1o619AOP2BZ/NEnOyT5QGdqXUrHakZS4Eds3YlVJzyNHWPlwOYUlV/pZi0q2xa2BXSs1qR1r6WFJVRIEzf8OZ1tiVUnPKkZY+ltfkbxkGtMaulJpjGruC1Ffm36CkVD6PZuxKqTkiHjf0R2L40qxBzzaFWopRSs0Vwag1GjPdwDfb+LQUo5Sabqc7Ammt+JOpRBvpdgecbQrTnCZBA7tSKqeisTjXfv1J7tx6YtLbSsx4mI/zw6RyOITff+CKie8/ieeilJqDmrqD9IWitPeFJr2tYMQuxeR5YAe4YFHZhPfVwK6UyqmzXf0A9EcmvxQTmCOlmHRpYFdK5dSZzqkL7P1zKGNPhwZ2pVROnem0VjIKTkVgD8+NXjHp0sA+Q/QGI9N9CkrlRDJjn4peMREN7CPRwD4DPHe8g/Wfe4gfPHV8uk9FqawlMvYprbEX5PcApXRpYJ8B7t/TSNzA/7t/P48ebJ7u01EqKwM19vikt5X48PC6NZSl0ldjmhljeORAC1eurGZ+qZe7nz8z3aekVMaisThN3UEAglMyQMla4DndSbLynQb2afZicx9nu/q57oI6Ll9exXPHOzDGTPdpKZWRcz1BYnGDyBT1iglb3wq0V8xgGtinQSQW56sPv8jZrn4e2HsOgKvPm8clSytp6wtz3+5GPnrXLsLRyf8qq1QuJcowiyuKpqbGHonidjlwOmTS25pNNLBPgz++0MQ3HjnMR365k+8/dYyrVtdQW+ply7IqAD7+6938dudZDjT1TPOZKjW+UDTGTbdv5b///CJn7cC+cl5xVqWYrkCY9/3keTr94TH3C4Zjmq2PQAP7NPjhX0/gdjnYdqKTvlCUf7nufAAaqoqoKfEQiVmlmP0a2NUs8LWHD7PtRCdbj7RzprMfEVhW48sqY9/X2MMD+86x63TXmPsFwjEddToCveIwxXad7mLX6S4+85o1bDvRwaraElbVlgAgIrzmwjpebO5lz+lu9jdqYFczW0tPkP994ihOh3Cyw8+SziJqS7yUFRYQjRvC0ThuV/r5YyRmlSG7+8ce39Ef0Yx9JBrYp5Axhi8/eJDyogJu2ryI91yxdNg+//batQC86fanNWNXM96Zrn6MgfWLy9hxqovDLX0srChMzrbYH4llFNij9rfWrsDYpZj+cEwHJ40gJ6UYEblGRA6JyBER+WQujpmP/nyghb8eaeejr1hFibdgzH3XLCjlQFMP8bj2kFEzV0efFXg31lcA8MLZbhZVFCaDbabTCkTjiYw9OuZ+mrGPLOvALiJO4FvAtcAa4C0isibb4+aj7z55jKXVPt66pX7cfdfUlRIIxzjZEZiCM1MqMx32xc1NdmCPxY0V2BMZe4YXUKN2QjO0FDO0K3BAM/YR5SJjvwQ4Yow5ZowJA78EXp+D447IGENPMJL898KZbn7w1HFaeoKT1WRO+ENRdpzs5Jp18ylwjv+yr1lQCqB1djWjtfmtOdc31pcnty2qKBoI7Jlm7LHhgb0vFOXiLzzCH19oSm4LasY+olzU2BcCp1NunwG25OC4I+oJRln/uYeGbf/240e5YdNCVtaW8MaNC3GM0q/VH4rS3R+hrsyLyNT1fX3ueAfRuOGKFdUT2n95TTEAR1v7JvO0lMpKR18Yb4GDujIvJV4XvcEoiyoKkxl3poF94OLpQI39bGc/bX0hvv34Ea5dNx8R0V4xo5iyi6cicitwK0B9/filiNF4XA4+/X/OT94uLSxgeY2Pz9y7jx9uPUE4GufOrcdZVF5EeVEBDofQH47hcTk429XPc8c7CEXjVBd72FhfzobF5dRXFuEQQQQEWFxZxPl1pYMGPcTjhr5wlP2NPfxq22kaqn28/6rlw7Lv/nCMXz9/mtdvWEgoGuNrD7/I44daqa8swuNycNGSigk9z0K3k7oyLyfa/Bm/VkpNtg5/mCqfBxFhSVURe8/2sKiiiOae7KYVGKkUk1iRae/ZHnae7mJTfYVVY9fAPkwuAvtZYHHK7UX2tkGMMXcAdwBs3rw54yuC3gInt1y5bNj2P3zoSowx3P38Ge7ceoJjbX10BiIYYyh0OwlG4swr8XDzxYtZVlPM7jNd7DrVxcP7R550y1vgoLbUS4c/TH84lnyjAfjcTvzhGA/uO8fnXreWzQ2VADT3BLn1x9vZfaabtt4QR1r7+PP+Fip8BTx7vIMrVlSntTZjQ5WP4+0a2NXM1e4PU1XsBmBJlY+9Z3tYUO6lxw7ImZdihnd3bLUDuwj87JlTVmAPxyjUmR2HycUrsg1YKSJLsQL6zcBbc3DctIkIN21ezE2bF4+/s63TH6atL4QB4sYQixtebO5l79kemnuCVPnc+DwuCpwOitxO6iuLeOnqGp54sZXP3refG29/mu+9YzNxY/jEPXsIReIsrfZx9/NnaOsL87ZL63nvFUu55UfbuWnzorSeT0O1jwf2No2/o1LTpN0forrYA8Cr1tTiFMHjciaz6MxLMSNl7FZZ5ooV1TxxuBVjjJ2x6zjLobIO7MaYqIh8AHgQcAI/MMbsy/rMpkiFz02Fzz1o29oFZVy/cezHXbOujitX1nDT7U/zkbt24Q9HuWBhGV978waeP9nJJ+7eA8ANmxaxqKKIBz7yN2mf29LqIjoDEboDEcqKxu4eqdR06OgLJwfYvX7DQl6/YSFADnrFWBl7V2AgsLf1hXA5hOsuqOPJw23sb+ohFjc6s+MIcvJRZ4z5ozFmlTFmuTHmC7k45mzg87j4xlusT4CXrZ7HXbdexvKaYq5ZNx+3y8F580tYa/duyURDlQ9AyzFqRjDGsK+xe9Dtdn+YqiGJEZAsOWbajz2RsYeicY619vFicy/tfWEqfe5kB4RHDrQMaksN0I+6LK2YV8wz//JyfG5nspdNqbeAr9y0PuueN0ur7cDe1seGxeW5OF2lMvbYoRbec+d2fv2+y7i4oZJAOEYoGqfKLsWkyrYUk+juCPDhX1rfiJdW+agu9rC4sohFFYU8csC6Pqa9YobT4lQOFHtcwwL4a9cvSF5UzdTiyiJE4MsPHOLG72zVedrVtNp2ohOARw9amXJicFLlSBm7PY1AYr70dCVKMWCNZj3R5udsV3/yQu1Lllez+4z17UH7sQ+ngX0G8xY4WVRRSGN3kO0nOzmuXR/VNNpzpguAJw+3AlbNGxixFONyOnA7HVlfPE2IGzjU3EuN/e3gHZcvSd6n3R2H08A+w/33mzfypRsvBGDHqa7pPRk1Z8Xjhj1nunE7Hew920NbX2jMjB2sLsMZzxUTG57pG0MyY1+7oIw3bFgAaMY+Eg3sM9xFSyq4cdMiSjwudpzqnO7TUXPUiXY/vcFossvuU4fbaLcDe/UINXawMuls54pJSFQ6U9v68k3ruf1vN3H58qqM2shnGthnAYdD2FBfztYjbVzz309w229fSA65Vmoq7LHr2W/dUk+R28nuM100dQURgZqSUQJ7gTPzi6fxOC575HdDVRH1lUUAgy7UFjgdXLOuDtcE5l6aa7RXzCyxqb6CJw+34XQIB8/10h+J8dU3bZju01JzxL7GbjwuB6trS1ha7eNYq5+ywjALygpH7W7ozSawxwwVPjetvSHWLCglFIlzsj1AdfHIZR81mH7UzRKX2uuh3nbd+bxtSz33727K+GuuUunqCkSo9LlxOR0sqynmWFsfx9r6WFbjG/Ux1lQeE3uPRmJx/vV3e5NzI0ViBrfTwc0XL+aGTYtYMc+aFG+0so8aTAP7LHHpskr+/LGX8u6XNPDKNbWEY3GeO9Ex3ael5ohAymRby6p9nOns52iLPzkL6UiK0qixv3C2m588c5I7t54ArFJMgVP44g0X8vLza7m4oZIit9VLTI1PA/ssISKsmFeMiHDJ0krcTgd/PdI23ael5ohgeGDe8+XzijHGGny0fIyMva6skD1nu3nq8Pjv0332ugMP7D1HPG6Ixsyg2vkr1tSy8zOvpLxISzEToYF9Fipyu9i0pHxCfzBK5ULqvOfLqgeC+bIxMvbbrjufZdU+bvnxNp451j7m8ffbUxWc6wmy+0wXkdjAxdMEj0u7NU6UBvZZ6ooV1exv6qG1NzTdp6LmAKsUY/W1SK2rj1Vjr/C5+ektW1hUUcR77tzGi829o+67r7GHdQtLcTmEB/adIxo3uJxTtxBOvtHAPktdfV4tQHK+DKUmk1WKscJFkdtFXZmXIreT+aXeMR9XXezhZ7dsIRKLc8+OMyPuE4nFOXiul0uXVrFiXjFHW/x2xq7hKVPa3XGWOr+uhEUVhTy0v5nyIjeVPjeXLM1ubhqlRhOIRAdNj7umrpSu/siEJrmrLfWysb5i1GtCR1v7CEfjrF1YyraTnYRjcaIxQ4Fm7BnTwD5LiQivXjufH209wWOHWigrLOCxf7xq2NzySuVCfzg+aE6WL914IbH4xCelu2JFNV/784t0+MPDpiA40GRdOF27oAyPy0EoEiNujGbsWdBXbhZ71ZpaonFDQ5WP3mCUrzx8aLpPSeWp/nB00JwsVcUe5o1Thkl1xcpqjIGtR4dn7S091nWiBeWFeFwOwrE4kZjW2LOhgX0Wu7ihkn+57jx+8t5LePPFi/nlc6d10JLKOWMMgUgsq3nPL1xYRonXxV+PDO8d0xmIUOAUfG6nnbHH7X7sGp4ypa/cLOZwCLf+zXIWVRRx1aoaonHD/qbu8R+oVBpC0TjGZDc9rsvpYFVtSXJkaaru/jBlhW7EXi81FI1Z/dgdmrFnSgN7nlhvr7C067QGdpVbiW+B2U6PO7/Uy7me4LDtnf4IFfaavolSTDRuNGPPgr5yeaK21EttqSe5GAJYc2hPx+IczT1BWkb4A1azU8Ce7yXbJejml3k51x0cthJYZyBMhT2i1FNgl2Jica2xZ0EDex5Zv6g8Ob0qwJcfOsTLv/I4Z7v6p+wcQtEYN93+NO/76fODtsfjRqcanqUSGXu2i0bXlXnpj8To6Y8O2t7dH6HMztjdTgehqH3xVHvFZExfuTyyfnE5x9v8/OXFVu55/gz/+5ejxA3sOzu55Zm2vhBHWqxRhT/eepJTHQF2nu5KLp0G8Pk/7OclX3yUIy19k3ouKvcSgT21H3sm5pdZvWiaegYnGlbGbpdiCuwauz0JmMqMBvY8sqm+AoB3/uA5/vHXu5OjAscayp2prkCYT//uBU53BPjgz3dy/be2crLdz/88ephlNT6Mgb8cstbG7A1G+OVzp2npDXHzHU/z6d+9MKXfIlR2+nNVirHfj+e6rTLdF/6wn2eOtdMViCQn9/K4HISj1gAlp148zZgOUMojly6r5Bd/dylxYyhyO1kxr5jrvvEkh5pznyXf/pdj/PSZU2w92s6xVquOf9PtT9MXivKr913G27//HI8dauENGxdy3+5G+iMxvnTDhfx+TyM/feYUlUVuPvaq1Tk/L5V7gbBVOsm2FJPI2M91BwlGYnz3yeO09oYIReOUp5Ri4vbMkXrxNHNZBXYRuQn4LHA+cIkxZnsuTkplRkS4bMj6j6trSzh0rien7TT3BPnx0yeYX+rlWKufSp+btQtKefJwG2+5pJ7z5pdy1aoa7t5xhvv3NOF0CKtrS7hp8yLedPFiXvLFRznTOZCxx+MGh2ZnM9ZAKSa7wD6vxIsINHUHk5PXJa4JpV48BWs2Se3umLlsM/a9wBuB/83BuahJsKq2hMcPtRKOxnG7BmdATx9tZ/eZLl61ppaD53rpDUaIxSFmDAIsLC+k3R9mcUUhW5ZVEY8bPvTLndy/pwmHwL3/8BLueOIYV6ysZvX8Er7whwN87JWrALjlymUUuBxU2cubvXb9guS8IgsrCpOB/cXmXt763Wf5zGvX8Lr1C6b0tVETk6tSjNvloLrYw7nuIK329Zdjdq+t8sJEd8eBNnQt08xlFdiNMQeACU0EpKbH6vklROOGd/3wOSKxOK9dv4C3X7qESMzwsV/toqk7yBf/dHDMYzgE7nj7ZvY19nD/nibecdkSbti0iJW1JXz5pvXJ/X7y3i2D2v2P6y8Y8XiLKgp55qg1AvGrD71IW1+I237zAn893EZLb5B3Xt7AVavn5eDZq1wI5KgfOwz0ZU9MI5CQqLGnJh968TRzWmPPc6vnlwCw7UQHy2uK+cy9+ygscBI3hqbuIJ973VrixrBhcTk1JR5cDgcOB8TjcKYzQJHbxT/dvZtbfmxV2V5zYR2fe93arD7MF1UUca7nLLtOd/HAvnPcdNEi/rT3HL/ddZaKogLe9cNtPPzRv2FlbUlOXgOVneQApSwzdrDq7KfaA8mMPaE8ZYBSgnZ3zNy4gV1E/gzMH+Gu24wx9060IRG5FbgVoL6+fsInqLKzcl4JH7p6BVeuqmFTfQVv+94z3Pa7vbgcwrqFpbzjsiWjBunExa7vv/Nifvz0CVbPL+GadfOz/oa2qKKQuIFvP3YEt8vBv752DR+8eiVet4OuQIRXfe0J9jf1zNnA/rpvPsU16+bz91etmO5TAQZKMbnI2OvKvDxztJ3WIQPYkjX2QaUYzdgzNW5gN8a8IhcNGWPuAO4A2Lx588Tn+1RZcTpkUO+Tr9+8kf984CAuh3DLlcsmFKTnl3n5xDXn5eycFpVbCxI/dqiFjfUVlHoLKPVaGVuptwARpmXE7EzQF4qy50w3PrdrxgT2QDiG2+nISc17abWP3lCU/U09FDiFSMwKBcleMVqKyQktxcwxtaVevvqmDdN6DosqigCIxAxbhiwO4i1wsqCscM4G9pPt1vPe39SDMWZGXL8KRmI5KcOA9Q0S4NljVmnwhP18E10ptRSTG1m9ciJyvYicAS4D/iAiD+bmtFQ+m1/mJdGTbaRVn5bV+OZwYA8A1jD7xu6ZMd9OIBzNukdMwqpaa/Hr3lCU2lIv9ZVFyTIMDA7smrFnLqvAboz5rTFmkTHGY4ypNca8OlcnpvKX2+VgfqkXp0OSo2VTNVRZgX3oZFFzQSKDBTjQmNvxB5kKhGM5qa8D1JR4KPW6kj+fX1fKYvsbHAwuxWh3x8xpKUZNi+XzillQXojPM/wtuLTaWhGqqTvIT545yZ4zXdz57kvmxEjEE21+Srwu+uw69CvW1E73KeW0FCMirKotYfvJTuaVeHj/VcsHLbGXevFUpxTInAZ2NS2++qYNGEbOyJfW+AC48Ttbk+WItr4QdWWFU3Z+0+VEe4DVtSV0+MPsn0EZe65KMQArUwJ7iX3RPCEx8hS0FJON/E+B1IxUU+JhXsnIa2Yuq7YCe2N3kNfao1E7/ZEpO7fpdLLdT0O1j/PqSjg0CZO3ZSIQjmU9T0yqlfOsOnvNCL9/t1MvnuaCvnJqxllYXsiyah8ff9Uq/naLNeahMxCe5rOafIFwlOaeEA1VRdSVFdI8QxYrCWa53ulQG+vLAesi+VCaseeGlmLUjONyOnj041cBA1MOd/jzO7Dfv6eRj/96NwBLqny4nP0EwjH8oeiI1yGmijGGzkCYNXWlOTvmxvoKnrvt5SN+Yxs0QEkz9ozpK6dmtERXuK48z9h3n+4iHoePvmIVV583j+piD8CgxUqmw9HWPpp7QmxaMrz3UjZGK8MN6seuGXvGNLCrGS0xIrEjz2vsXYEIVcVuPvyKlfg8LqqLrQ+0qQ7sv9/dmFwIA+CRAy0AXH3e1EzKllpjnwu9oCaLvnJqRitwOijxuvKmxn6mM4A/FB22vas/QlnhQA+RmhIrY2/tnbrn3RuM8MFf7OQXz51KbnvkQAtr6kpZUD41PZIcDkkGd52PPXMa2NWMV1HkzovAHozEuO7rT/KlB4ZPk9w9NLDbpZihsyBOpqaUrqUA3YEI20928PLzp3YK5UQ5RgcoZU5fOTXjVfjcdAZmfynmiRdb6QlGeeRgy7BRtd2BSLLsBFDpcyMCbb1TH9gTF6r3NXUTN7BladVYD8u5xOhT7RWTOQ3sasarKCqgMw96xTyw9xwAZzr7uW93I2///rPJ9US7+yOUFw7MmeJyOqgsck9txm4vMN5uv9ZHWqy1chPzu0yVZMauvWIypq+cmvEq86AUE47GefhAM5vt3iUf//VunjzclpzsrKs/TFnR4FGY1cWeKc3YG4dk7Ieb+yj1upL1/qnisQdDaa+YzGlgVzNeeZF71mfs20900BuM8n9fupyF5YXJeci7AxGCkRjBSHxQjR2sC6hTmbGf67Yy9sRrfbill5W1JVM+dbBePM2eBnY141UUFeAPxwhFY9N9KhnbdqITEdiyrJIbL1qUHHXZ1R+hp9+6fjA0sFcXu6e0u2Oixt4ZCBOPGw439yWH/0+lxOhT7e6YOX3l1IxX4UsMUpq9F1C3n+xgdW0Jpd4CPvrKVfz8lksBK4h22YG9vGh4xt7WG56y6Ysb7Rp73MCxNj/t/jArpiOwJ3vFaMaeKQ3sasZLjD6drXX2aCzOjpOdXNwwsKhIIoh3BSLJD6zUi6dg1dj7IzH84cn/pmLsxc0TI16fPd4OMC3rzrr14mnW9JVTM16FLzH6dHYG9oPnevGHY2xuGBiW7y1w4i1w0N0foXuUUkziouXZzv5JP8eeYJRAOMbaBdacMM8e6wCYnlKMPV+MdnfMnE4Cpma8SrsUM1un7t1+wgqSqRk7WBl6pz+cnAdnaClmyzKr//hD+86xev7kZM5PHW6jsas/Wddet7CUv7zYypOHW6koKqCubOQ5XSaTDlDKngZ2NeMlJoyaKdPYputkRwCf2zlsWH55UQFdKRl76ZCMfWF5IRc3VHDf7kY+cPUK7tvdyLcfO8ofP3xlzlYX+shduwZdoL1gYRkAnYEIf7OqZloW0x4oxWjGnikN7GrGqygqwONy0NQ9+SWJsXT6w+w600Wp18VFS4Yvwj2aDn+YquLhfcHLiwroCoTp7o/gECgZYXre161fwL/eu4+D53p5aH8zh5p76fCHc9K3PBiJ0dYX4p2XLeGy5VV0+CNctXpg+oD1i8qybiMTHpf2ismWvnJqxhMR6sq8ye540+Vjv9rFu3+4jRu+8zSnOwITflx7XzhZTkpVUeROXjwtLSzAMUKGet0FdTgE/vRCE7tPdwG5m/Ex8XpeuKica9bV8dYt9XgLnMkPmET2PtU8LiciuuZpNjSwq1lhfpl30HSyufbCmW62Hm0btj0Si/OHPU10+sM8daSNS5ZamfruM10TPna7P5ychjdVaimmfEgZJqGq2MPG+gp+t6uRM/ZF1NYcjUZNdG8cWiKqtM91/eLynLSTLrfLQYH2iMmKvnpqVqgrK5zUjP2LDxzgn369Z9j2nz5zkn/4+Q5u+fF2IjHDR16+kgKn8MLZ7gkfu8MfGjFjLyt00xUI0xkIU1Y0/P6El62u4VTKN4RcZewDgX3wBdJKn5vaUg+1pVN/4RTg2nXzed9Ll01L2/lCa+xqVqgr89LcEyQWN5PyFf1Yq5+m7iDdgUhyzpZgJMZ3Hj8KwPMnO6kudrNlWRWrakvYd7ZnQsc1xoxaY68oKiASM5xo97OiZvRuhVetnsd/PfRi8nbuArv1QTl/SM+Xmy9eTP8U9J0fzeaGSjY3TPwahhouq4xdRL4sIgdFZI+I/FZEynN0XkoNUlfmJRo3OQlqdz9/hq1HBsou/eFY8tvA/iYrYPcEI3zm3r209Ib49zesw+UQXrmmFqdDuGBhGXsbuyc0IrQnGCUSM1SNkLEnujee7ugfs+yxdkEp80o8rK4tweNy0NaXm/78Td391JR4Bq0zCvDmi+t510uW5qQNNT2yzdgfBj5ljImKyH8CnwL+OfvTUmqwujKrDtzUHcyqRLDzVCf/dPdu1tSV8ocPXQnAiXZ/8v4DTT209Ab5/P0HaPeHeM9LlvK3ly5h/aJyFlda57B2YRm/3Haas139LKooGrO9dvuDqGrEGvvAtkTtfiQiwhdvuIACp4NP3vNCzmrsZ7v6WTAN/dTV5MsqsBtjHkq5+QxwY3ano9TIEuWCc939kOFFvUgszqd+8wLGwL7GHpq6+6krK0xOnSsCd207zaHmXtYvLuf779yczKQvSOn6t84enbnnTPe4gT0xWrbSN0J3R/uCaYFT2FQ/9mLRV59XC9jzx+SwV8x0jCxVky+XF0/fA/wph8dTKikxAjJRFwbYeqSNU+3WRcVgJEan35qVcDTff+o4B8/18k+vXg0MLNScCOwX1VdwqLmXSp+bX/zdllHLI2sWlFLlc3P382fGPe/EohUjl2Ls3ieLyvEWOIfdP5LqYk9OMnZjDI1d/clvQiq/jJuxi8ifgfkj3HWbMeZee5/bgCjwszGOcytwK0B9fX1GJ6vmrkqfG7fLwTn7Aurn79/PnVtPIGL1B09kxt4CBw1VPmpKPPSHYwSjMVbNK2FptY9vPX6EV62p5e+vWs5d207zyIFm/vbSJRxv81Nb6uGihgq2n+zk3Zc3UOQe/U/D43Ly9suW8N9/PsyRll5WzBt9uH+7XQ8fqRSTmANnrDLMUDUlbnbZ/dmz0dNvzQ0ztEeMyg/jBnZjzCvGul9E3gW8Bni5GeNqkjHmDuAOgM2bN0/NPKQqb4gIDVVF7D3bzb27znLn1hO86/IGyosKONcdZFFFIYVuF01d/Ry3p5wtcjsp8rh47FALv9kZYWF5IZ993VpEhFecX8tPnzlJdyDC8TY/S6t9vGpNLTtPdvGOyxrGPZ+3X7qE7zx+lDu3nuDf33DBqPt1+K3seqTujvNKvPzH9RfwyjW1E34dqos9dPhDWfcOauweuQ+7yg9Z1dhF5BrgE8BLjTETH4qnVAauWVfH/zx6mM5AhMWVhfzba9dMaC6TeNwQjsXxuBzJ/d+4aSE/+Otx7tlxhqOtfVy7ro6LllTyq/ddNqFzqSr2cOXKarYebR9zv3Z/mBKPa1jPk4S3bknv22tNiYe4IetpBRLz7kxXX3U1ubKtsX8TKAEeFpFdInJ7Ds5JqRFdv3Ehxlg9V964cdGEJ6hyOARvgXPQ/msXlHLe/BL+vz8doCsQ4ZVr5o1xhJFtWFzOsVY/3WMsANLeF06O5MyFxHzp2V5AbemxHl9bOrXrmaqpkVVgN8asMMYsNsZssP+9L1cnptRQS6t9bLAvaN6waVFWxxIRbtq8mEjMcNNFi5K9TtKxYbHVk2Ws6QU6/OERL5xmKhHYP3vfPvamMfp1qHN2xj7VC1WrqaFTCqhZ5ZPXnscnrz2P+qqxuxlOxM0XL+afrzmPf3vd2owef+HiMkQY9WLmtx47wl+PtuV0ebkLFpZx40WLOHiul/93//6Mj9PcE6TS5x61RKRmNw3sala5dFkV73vp8pwcy+dx8f6rllM8wnS5E1HqLWBFTfGIgf1ku58vP3iIa9fN57MZfnCMpNDt5L9uWs+7Lm9g24kOWnozmz+nuSfEPM3W85YGdqWysGFxeXI63VQ7T1nbPnj1yjG7TmbqugvqMAYe2tec0eNbeoPD5ohR+UMDu1JZWD2/hPaU5e0Sdp3uosjtZNUkLQa9qraYZTU+/rS3KaPHN/cEqS3RwJ6vNLArlYX6SqvWf6J9cG/fnae7uGBh2aQtFiEiXLmimt2nJzYZWUJrb4jmniCtvSHtEZPHdNpepbLQUO0DrJp6osdOKBrjQGMP776iYVLbXlLloy8UpTMQGXEA1FB9oShv/M5ficUMcQPztA973tLArlQWEhn7qZSMfX9jD+FYnI2TvALR4kTbHYFxA3tLb5Av/vEgpzsG1o3VwUn5SwO7UlnwFjipLfVwsiPA73aepcDp4Id/PY7b6UhrwetM1KcE9g1jfIhsPdrGO77/HNG44a1b6vn19tNEYkZLMXlMA7tSWVpS6WPHyU7u2XEGY6zpf7/11k2TPvgnMT/8aAtr3/bbFzivrpTHDrZQ4XPzg3dezLqFpbT1hnhofzPzNWPPWxrYlcpSfVURz53oAOCzr13DkmofL1ud/hQF6Spyu6gu9owY2IORGL947hROhxCNGz7wshXJOeU/9PKV1JZ6k6NYVf7RwK5UlpbYJZGGqiLeeXnDhOewyYX6ysJBC10nHDzXS9xAPGZwCNx8ycBkY+sWlrFuYdmwx6j8oYFdqSwlpjd49dr5UxrUwaqzbz/ZOWz7/kZr7dYv3XghoWichTo975yigV2pLG2qr2BBmZfrNy2c8rbrK4u4b3cjkVicAufAsJR9jd2UeF3cdNHEZ8FU+UMDu1JZWlxZxNZPvXza2o4bONvZn+xTD7C/qYc1daUa1OcoHXmq1CyWKLE0dg30T4/FDQebelm7QOvoc5UGdqVmsbpEYO8emOXxeJuf/kiMNQtKp+u01DTTwK7ULFZnz9DYlJKxH2npBayJwtTcpIFdqVnMW+Ck0ucelLEfbfUDsLxGA/tcpYFdqVmursxLU3dqxt7HgjIvvgwXEFGznwZ2pWa5urJCmrpSM/Y+ludwOT41+2hgV2qWW1DupdHO2I0xHG3p0zLMHKff1ZSa5erKCukNRukLRenpj+APxzRjn+M0sCs1yy0oH+gZc67HKsms0Ix9TtPArtQsV1c20Jf9cLPV1XH5PN9YD1F5Lqsau4h8XkT2iMguEXlIRBbk6sSUUhOT6Mve2NXPn/aeY1VtMTU6Je+clu3F0y8bYy40xmwA7gc+k/0pKaXSUVfmZV6Jh+8+cYznT3Zywyad+GuuyyqwG2N6Um76gIkvl66UygmX08Enrz2PY21+HAJv2Dj1s0yqmSXrGruIfAF4B9ANvCzrM1JKpe36jQu5Z8cZij0uXaRaIcaMnWSLyJ+B+SPcdZsx5t6U/T4FeI0x/zbKcW4FbgWor6+/6OTJkxmftFJquHjcIIKWYfKYiDxvjNk87n7jBfY0GqwH/miMWTfevps3bzbbt2/PSbtKKTVXTDSwZ9srZmXKzdcDB7M5nlJKqexlW2P/ooisBuLASeB92Z+SUkqpbGQV2I0xN+TqRJRSSuWGTgKmlFJ5RgO7UkrlGQ3sSimVZzSwK6VUnslZP/a0GhXpBQ6lbCrDGrk61GjbAaqBtjQfk247o7WRybHSeUxqu5PZzkjtZXus8R5TMEpb6baTTvuJ55fL12ys+3L5ek6k/aHtTcXzrAYiOTrWeI+Zir/11Psm+npOx/NcbYwpGeXxA4wxU/4P2D7k9h2j7Dfi9pGOMcHHpNXOaG1kcc4Tekxqu5PZznQ8z3R/b7loP9FmLl+zqXo9J9L+RP+ecvk8ge1T8d4c6/WcrOeZo/g0Kc9zrPdW6r+ZUor5fZrbMznWVLWT6/an6jHT3X66j5nu9se7L1ftzOTnOVPfm5m2Mxuf54imqxSz3UxgWOxkH2MmtDET2p3K9qbjNc3n13M62pvqNufC6znRdid6btOVsd8xQ44xE9qYCe1OZXvT8Zrm8+s5He1NdZtz4fWcaLsTOrdpydiVUkpNnplSY1dKKZUjGtiVUirPzNjALiIxe5HsxL+GMfZ9XETSvtghIkZEfppy2yUirSJyf4annW77b7DP4bxJbGNanqOI9E3m8TNtN9P3ypBjTPrvbYQ2bxORfSmLx2+ZgjYXici9InJYRI6KyNdFxD3G/h8RkaI02zAi8pWU2x8Xkc9mcdrjtZeIK/tEZLeI/KOITGkcnIq/jRkb2IF+Y8yGlH8nJqENP7BORArt268EzqZzABHJZobMtwBP2f+n06Yzjd2zfo5qmIx+b5kSkcuA1wCbjDEXAq8ATk9ymwL8BvidMWYlsAooBr4wxsM+AqQV2IEQ8EYRqc7kPDOQiCtrsf4WrgVGXPVtNpvJgX0YEblIRP4iIs+LyIMiUpdy99vtT+K9InJJGof9I/B/7J/fAvwipb1LRORpEdkpIlvtuecRkXeJyH0i8ijwSIbPpRi4AngvcLO97SoReUJE/iAih0Tk9kQ2ISJ9IvIVEdkNXJZmc5k8xydEZEPKfk+JyPo0n+NVqd8MROSbIvIu++cTIvI5EdkhIi/kMvsdq90cHHu039toz/M6ETlov2e/keE3pTqgzRgTAjDGtBljGkf7e7C/lXw9w7+HhKuBoDHmh3abMeCjwHtExCci/2Ufe4+IfFBEPgQsAB4TkcfSaCeK1dPjo0PvEJEGEXnUbuMREakXkTIROZnyd+ETkdMiUpDuEzTGtGAt1/kBsThF5Msiss1u8/+mnMs/2+/T3SLyxXTbGuG5FdvPKfH+f33Kcz4gIt+1v1U8lJKUTdhMDuyFMlCG+a39i/sf4EZjzEXADxicPRQZYzYAf2/fN1G/BG4WES9wIfBsyn0HgSuNMRuBzwD/kXLfJvtcXpruE7O9HnjAGPMi0C4iF9nbLwE+CKwBlgNvtLf7gGeNMeuNMU+l2VYmz/H7wLsARGQV1nq2u9NsdzxtxphNwHeAj+f42JNltN/bMPbr/b/AtfZ7tibDNh8CFovIiyLybRF56ST+PSSsBZ5P3WCM6QFOAbcADcAG+xvEz4wx3wAagZcZY9Jd1P5bwNtEpGzI9v8BfpRoA/iGMaYb2AUk/u5eAzxojImk2SYAxphjgBOYh/Vh3W2MuRi4GPg7EVkqItdi/d63GGPWA1/KpK0hgsD19vv/ZcBXRJKL1a4EvmV/q+gC0l73ItsVlCZTv/3GBEBE1gHrgIft5+8EmlL2/wWAMeYJESkVkXJjTNd4jRhj9ohVv38LVmabqgz4kVhLABqseU4SHjbGdKT7pFK8Bfi6/fMv7dv3A8/ZbzZE5BdY2eHdQAy4J5OGMnyOvwb+VUT+CXgPcGcmbY/jN/b/zzPwATbTjfZ7G8l5wDFjzHH79i+wF3RPhzGmz/4AuRIrCNwF/DuT8PcwQVcB3zbGRO02svk7wBjTIyI/Bj4E9KfcdRkD74ufMBBQ7wLeDDyG9a3p29m0n+JVwIUicqN9uwwryL4C+KExJmCfb1bP1ybAf4jI32CtQLcQqLXvO26M2WX//DzWh2haZnJgH0qAfcaY0coQQzvkp9NB/z7gv7DesFUp2z8PPGaMud4OjI+n3OdP4/iDiEgl1lfdC0TEYP1RGuAPI5x34nbQ/jqcqbSeozEmICIPY2UqbwJGzUzHEGXwt0LvkPtD9v8xcvteHK/djIzxe7t3MtpLZf/uHwceF5EXgH9g8v4eAPYDN6ZuEJFSoB44keaxJuK/gR3ADyew731YQbES6335aKaNisgyrPdfC1aM+aAx5sEh+7w60+OP4W1Y3+AuMsZEROQEA++bUMp+MSCvSjFDHQJqxLqQhIgUiMjalPvfbG+/Auvr1GgzqI3kB8DnjDEvDNlexsCFxndldNYjuxH4iTFmiTGmwRizGDiOlZFdYn/9c2A9p3TLLqPJ5Dl+D/gGsM0Y05lBmyeBNSLiEZFy4OUZHCMTk9XuaL83xyjtHQKWyUCPrjdn0qiIrJbBC8dvAA4weX8PYF07KhKRd9jHcQJfwfrm9iDwf8XuOGAHWIBeYPyZB0dgZ8G/wiqHJGzFvo6BFQiftPftA7ZhfXO6P9OER0RqgNuBbxprpOaDwPsT9XoRWSUiPuBh4N1i9/hJeb7ZKANa7KD+MmBJDo6ZNGsydmNM2P6K9A27FufC+pTfZ+8SFJGdWKWE96R57DNYAWyoL2GVKT6NlU3nyluA/xyy7R7g/Vhv2G8CK7C+av42Fw1m8hyNMc+LSA8Ty6KS7D/4kDHmtIj8CtiLFQB3ZnTyM6fd0X5vN2MFpUHtGWP6ReTvgQdExI/1u81EMfA/9odGFDiCVdK5g0n4e7DP3YjI9cC3ReRfsT68/gj8C1YWuQrYIyIR4LtY79k7sJ5rYwZ1drA+OD6QcvuDwA/tcmAr8O6U++7CKhdelWYbhSKyC+t1iWKVeL5q3/c9rLLHDrve3Qq8wRjzgFgdCbaLSJiB1yFtifco1jWD39vfvrZjXevKGZ1SYAYRkauAjxtjXjPNpwKAiCzA+vp/njEmnsbj1gPfNcZk0hsjY9PV7lhEpNiukQvWRcLDxpivTXKbj2O9j7ZPZjsqfVP1Hp1NpRg1heyv4M8Ct6UZ1N+HdeHu05N1bjOp3Qn4OztD3If19ft/p/d01HSZyveoZuxKKZVnNGNXSqlJICKLReQxEdkv1mCjD9vbK0XkYbGmanhYRCrs7W8Ta2DUC2INFlw/1nHGbFszdqWUyj2xRgLXGWN2iEgJVp/0N2D1PuswxnxRRD4JVBhj/llELgcOGGM6xRoU9VljzJbRjmOM2T9a25qxK6XUJDDGNBljdtg/92J1UV2INTbkR/ZuP8IK9hhjtqZ0K34GWDTOcUalgV0ppSaZPZZhI1aHhFpjTGKU8DkGRpymei/wp3GOM6pZ049dKaVmI7EmjrsH+Ig9fULyPnu8gBmy/8uwAvsVYx1nrDY1Y1dKqUlij2K9B2uitMTcSM0yMBNnHdZ0Bon9L8QaKPV6Y0z7OMcZlQZ2pZSaBPagtO9jXRD9aspd9wHvtH9+J9ZcQ4hIPdbEeG+3Zw8d7zijt629YpRSKvfseXqeBF7AmsERrKkInsWagqIea16jNxljOkTke1hT9J60940aYzaPdhxjzNCZWgfa1sCulFL5RUsxSimVZzSwK6VUntHArpRSeUYDu1JK5RkN7EoplWc0sKu8JiJb09z/KhEZbXFqpWYFDewqrxljLp/uc1BqqmlgV3lNRPrs/68SkcdF5G4ROSgiP7NH9CEi19jbdgBvTHmsT0R+ICLPichOEXm9vf3rIvIZ++dXi8gTYi0+rtSMoJOAqblkI7AWaAT+CrxERLZjLcZ8NdYi0Xel7H8b8Kgx5j32QtLPicifgU8B20TkSawFwq9LZ/lApSabZhlqLnnOGHPGDsK7sFakPw84bow5bKxh2D9N2f9VwCftNUsfB7xAvTEmAPwd8DDwTWPM0Sl7BkpNgGbsai4JpfwcY/z3vwA3GGMOjXDfBUA7sCBH56ZUzmjGrua6g0CDiCy3b78l5b4HgQ+m1OI32v8vAf4Rq7RzrYhsmcLzVWpcGtjVnGaMCQK3An+wL562pNz9eaAA2CMi+4DPp0yh+nFjTCPWggjfExHvFJ+6UqPS2R2VUirPaMaulFJ5RgO7UkrlGQ3sSimVZzSwK6VUntHArpRSeUYDu1JK5RkN7EoplWc0sCulVJ75/wEr1oKiMHkIOAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "account.curve().plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "10a52d1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "conn.close_connection()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
